Data Dictionary

  • Booking_ID: unique identifier of each booking
  • no_of_adults: Number of adults
  • no_of_children: Number of Children
  • no_of_weekend_nights: Number of weekend nights (Saturday or Sunday) the guest stayed or booked to stay at the hotel
  • no_of_week_nights: Number of week nights (Monday to Friday) the guest stayed or booked to stay at the hotel
  • type_of_meal_plan: Type of meal plan booked by the customer
  • required_car_parking_space: Does the customer require a car parking space? (0 - No, 1- Yes)
  • room_type_reserved: Type of room reserved by the customer. The values are ciphered (encoded) by INN Hotels.
  • lead_time: Number of days between the date of booking and the arrival date
  • arrival_year: Year of arrival date
  • arrival_month: Month of arrival date
  • arrival_date: Date of the month
  • market_segment_type: Market segment designation
  • repeated_guest: Is the customer a repeated guest? (0 - No, 1- Yes)
  • no_of_previous_cancellations: Number of previous bookings that were canceled by the customer prior to the current booking
  • no_of_previous_bookings_not_canceled: Number of previous bookings not canceled by the customer prior to the current booking
  • avg_price_per_room: Average price per day of the reservation; prices of the rooms are dynamic (in euros)
  • no_of_special_requests: Total number of special requests made by the customer (e.g. high floor, view from the room, etc)
  • booking_status: Flag indicating if the booking was canceled or not
In [1]:
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_objs as go
from plotly.subplots import make_subplots
import plotly.offline as pyo
import plotly.io as pio
pio.renderers.default = 'notebook'
In [2]:
df_reservations = pd.read_csv('hotel_dataset_kaggle.csv')
In [3]:
df_reservations
Out[3]:
Booking_ID no_of_adults no_of_children no_of_weekend_nights no_of_week_nights type_of_meal_plan required_car_parking_space room_type_reserved lead_time arrival_year arrival_month arrival_date market_segment_type repeated_guest no_of_previous_cancellations no_of_previous_bookings_not_canceled avg_price_per_room no_of_special_requests booking_status
0 INN00001 2 0 1 2 Meal Plan 1 0 Room_Type 1 224 2017 10 2 Offline 0 0 0 65.00 0 Not_Canceled
1 INN00002 2 0 2 3 Not Selected 0 Room_Type 1 5 2018 11 6 Online 0 0 0 106.68 1 Not_Canceled
2 INN00003 1 0 2 1 Meal Plan 1 0 Room_Type 1 1 2018 2 28 Online 0 0 0 60.00 0 Canceled
3 INN00004 2 0 0 2 Meal Plan 1 0 Room_Type 1 211 2018 5 20 Online 0 0 0 100.00 0 Canceled
4 INN00005 2 0 1 1 Not Selected 0 Room_Type 1 48 2018 4 11 Online 0 0 0 94.50 0 Canceled
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
36270 INN36271 3 0 2 6 Meal Plan 1 0 Room_Type 4 85 2018 8 3 Online 0 0 0 167.80 1 Not_Canceled
36271 INN36272 2 0 1 3 Meal Plan 1 0 Room_Type 1 228 2018 10 17 Online 0 0 0 90.95 2 Canceled
36272 INN36273 2 0 2 6 Meal Plan 1 0 Room_Type 1 148 2018 7 1 Online 0 0 0 98.39 2 Not_Canceled
36273 INN36274 2 0 0 3 Not Selected 0 Room_Type 1 63 2018 4 21 Online 0 0 0 94.50 0 Canceled
36274 INN36275 2 0 1 2 Meal Plan 1 0 Room_Type 1 207 2018 12 30 Offline 0 0 0 161.67 0 Not_Canceled

36275 rows × 19 columns

In [4]:
df_reservations.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 36275 entries, 0 to 36274
Data columns (total 19 columns):
 #   Column                                Non-Null Count  Dtype  
---  ------                                --------------  -----  
 0   Booking_ID                            36275 non-null  object 
 1   no_of_adults                          36275 non-null  int64  
 2   no_of_children                        36275 non-null  int64  
 3   no_of_weekend_nights                  36275 non-null  int64  
 4   no_of_week_nights                     36275 non-null  int64  
 5   type_of_meal_plan                     36275 non-null  object 
 6   required_car_parking_space            36275 non-null  int64  
 7   room_type_reserved                    36275 non-null  object 
 8   lead_time                             36275 non-null  int64  
 9   arrival_year                          36275 non-null  int64  
 10  arrival_month                         36275 non-null  int64  
 11  arrival_date                          36275 non-null  int64  
 12  market_segment_type                   36275 non-null  object 
 13  repeated_guest                        36275 non-null  int64  
 14  no_of_previous_cancellations          36275 non-null  int64  
 15  no_of_previous_bookings_not_canceled  36275 non-null  int64  
 16  avg_price_per_room                    36275 non-null  float64
 17  no_of_special_requests                36275 non-null  int64  
 18  booking_status                        36275 non-null  object 
dtypes: float64(1), int64(13), object(5)
memory usage: 5.3+ MB
In [5]:
# Copy for cleaning
df_clean = df_reservations.copy()
In [6]:
df_clean.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 36275 entries, 0 to 36274
Data columns (total 19 columns):
 #   Column                                Non-Null Count  Dtype  
---  ------                                --------------  -----  
 0   Booking_ID                            36275 non-null  object 
 1   no_of_adults                          36275 non-null  int64  
 2   no_of_children                        36275 non-null  int64  
 3   no_of_weekend_nights                  36275 non-null  int64  
 4   no_of_week_nights                     36275 non-null  int64  
 5   type_of_meal_plan                     36275 non-null  object 
 6   required_car_parking_space            36275 non-null  int64  
 7   room_type_reserved                    36275 non-null  object 
 8   lead_time                             36275 non-null  int64  
 9   arrival_year                          36275 non-null  int64  
 10  arrival_month                         36275 non-null  int64  
 11  arrival_date                          36275 non-null  int64  
 12  market_segment_type                   36275 non-null  object 
 13  repeated_guest                        36275 non-null  int64  
 14  no_of_previous_cancellations          36275 non-null  int64  
 15  no_of_previous_bookings_not_canceled  36275 non-null  int64  
 16  avg_price_per_room                    36275 non-null  float64
 17  no_of_special_requests                36275 non-null  int64  
 18  booking_status                        36275 non-null  object 
dtypes: float64(1), int64(13), object(5)
memory usage: 5.3+ MB
In [7]:
# Use the rename method to correct spelling of columns
df_clean.rename(columns={
    'no_of_weekend_nights': 'no_of_weekend_nights_booked',
    'no_of_week_nights': 'no_of_week_nights_booked',
    'type_of_meal_plan': 'meal_plan_type',
    'required_car_parking_space': 'has_parking_space',
    'room_type_reserved': 'reserved_room_type',
    'market_segment_type': 'market_segment',
    'no_of_previous_cancellations': 'total_cancellations',
    'no_of_previous_bookings_not_canceled': 'total_successful_bookings',
    'avg_price_per_room': 'average_room_price',
    'no_of_special_requests': 'total_special_requests',
  
},
                inplace=True)
In [8]:
df_clean.meal_plan_type.unique()
Out[8]:
array(['Meal Plan 1', 'Not Selected', 'Meal Plan 2', 'Meal Plan 3'],
      dtype=object)
In [9]:
df_clean['meal_plan_type'] = df_clean['meal_plan_type'].replace({
    'Meal Plan 1':
    'Bed & Breakfast',
    'Meal Plan 2':
    'Half Board',
    'Meal Plan 3':
    'Full Board',
    'Not Selected':
    'Self Catering'
})
In [10]:
df_clean['Booking_ID'] = df_clean['Booking_ID'].astype(str)
df_clean['meal_plan_type'] = df_clean['meal_plan_type'].astype(str)
df_clean['reserved_room_type'] = df_clean['reserved_room_type'].astype(str)
df_clean['market_segment'] = df_clean['market_segment'].astype(str)
df_clean['booking_status'] = df_clean['booking_status'].astype(str)
In [11]:
# Replace missing values in 'children' column with 0
df_clean['no_of_children'].fillna(0, inplace=True)

# Convert datatype of 'meal' column to categorical
df_clean['meal_plan_type'] = df_clean['meal_plan_type'].astype(
    'category')

# Convert datatype of 'reservation_status_date' column to datetime
#df_clean['reservation_status_date'] = pd.to_datetime(df_clean['reservation_status_date'])

df_clean['booking_status'] = df_clean['booking_status'].replace({
    'Not_canceled':
    'Confirmed',
    'Canceled':
    'Cancelled'
})
In [12]:
df_clean['arrival_date_time'] = df_clean[[
    'arrival_year', 'arrival_month', 'arrival_date'
]].apply(lambda x: '-'.join(x.astype(str)), axis=1)
In [13]:
df_clean.columns
Out[13]:
Index(['Booking_ID', 'no_of_adults', 'no_of_children',
       'no_of_weekend_nights_booked', 'no_of_week_nights_booked',
       'meal_plan_type', 'has_parking_space', 'reserved_room_type',
       'lead_time', 'arrival_year', 'arrival_month', 'arrival_date',
       'market_segment', 'repeated_guest', 'total_cancellations',
       'total_successful_bookings', 'average_room_price',
       'total_special_requests', 'booking_status', 'arrival_date_time'],
      dtype='object')
In [14]:
# Convert arrival_datetime to datetime format
df_clean['arrival_date_time'] = pd.to_datetime(df_clean['arrival_date_time'],
                                              format='%Y-%m-%d',
                                              errors='coerce')
In [15]:
df_clean.describe()
Out[15]:
no_of_adults no_of_children no_of_weekend_nights_booked no_of_week_nights_booked has_parking_space lead_time arrival_year arrival_month arrival_date repeated_guest total_cancellations total_successful_bookings average_room_price total_special_requests
count 36275.000000 36275.000000 36275.000000 36275.000000 36275.000000 36275.000000 36275.000000 36275.000000 36275.000000 36275.000000 36275.000000 36275.000000 36275.000000 36275.000000
mean 1.844962 0.105279 0.810724 2.204300 0.030986 85.232557 2017.820427 7.423653 15.596995 0.025637 0.023349 0.153411 103.423539 0.619655
std 0.518715 0.402648 0.870644 1.410905 0.173281 85.930817 0.383836 3.069894 8.740447 0.158053 0.368331 1.754171 35.089424 0.786236
min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 2017.000000 1.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 2.000000 0.000000 0.000000 1.000000 0.000000 17.000000 2018.000000 5.000000 8.000000 0.000000 0.000000 0.000000 80.300000 0.000000
50% 2.000000 0.000000 1.000000 2.000000 0.000000 57.000000 2018.000000 8.000000 16.000000 0.000000 0.000000 0.000000 99.450000 0.000000
75% 2.000000 0.000000 2.000000 3.000000 0.000000 126.000000 2018.000000 10.000000 23.000000 0.000000 0.000000 0.000000 120.000000 1.000000
max 4.000000 10.000000 7.000000 17.000000 1.000000 443.000000 2018.000000 12.000000 31.000000 1.000000 13.000000 58.000000 540.000000 5.000000
In [16]:
df_clean.describe(include='object')
Out[16]:
Booking_ID reserved_room_type market_segment booking_status
count 36275 36275 36275 36275
unique 36275 7 5 2
top INN00001 Room_Type 1 Online Not_Canceled
freq 1 28130 23214 24390
In [17]:
df_clean.duplicated().sum()
Out[17]:
0
In [18]:
df_clean.isnull().sum()
Out[18]:
Booking_ID                      0
no_of_adults                    0
no_of_children                  0
no_of_weekend_nights_booked     0
no_of_week_nights_booked        0
meal_plan_type                  0
has_parking_space               0
reserved_room_type              0
lead_time                       0
arrival_year                    0
arrival_month                   0
arrival_date                    0
market_segment                  0
repeated_guest                  0
total_cancellations             0
total_successful_bookings       0
average_room_price              0
total_special_requests          0
booking_status                  0
arrival_date_time              37
dtype: int64
In [19]:
# Check the distribution of numerical variables
num_cols = [
    'no_of_adults', 'no_of_children', 'no_of_weekend_nights_booked',
    'no_of_week_nights_booked', 'has_parking_space', 'lead_time',
    'repeated_guest', 'total_cancellations', 'total_successful_bookings',
    'average_room_price', 'total_special_requests'
]

df_num = df_clean[num_cols]
df_num.hist(bins=20, figsize=(15, 10))
plt.suptitle('Distribution of Numerical Variables', fontsize=16)
plt.show()
In [ ]:
# Calculate percentage of booking status
booking_status_pct = df_clean['booking_status'].value_counts(
    normalize=True).reset_index()
booking_status_pct.columns = ['booking_status', 'percentage']
booking_status_pct['percentage'] = booking_status_pct['percentage'] * 100

# Plot histogram
fig = px.bar(booking_status_pct,
             x='booking_status',
             y='percentage',
             text='percentage',
             color='booking_status')
fig.update_layout(title='Booking Status Distribution')
fig.update_traces(texttemplate='%{text:.2f}%', textposition='outside')
fig.show()
In [21]:
import plotly.graph_objects as go

# Group the data by market segment type and count the number of bookings
market_segment_data = df_clean.groupby(
    'market_segment')['Booking_ID'].count().reset_index()

# Calculate the percentage of bookings for each market segment
market_segment_data['percentage'] = (market_segment_data['Booking_ID'] / market_segment_data['Booking_ID'].sum()) * 100

# Create a bar chart of the count of bookings by market segment type
fig = go.Figure(
    go.Bar(
        x=market_segment_data['market_segment'],
        y=market_segment_data['Booking_ID'],
        text=market_segment_data['percentage'].round(2).astype(str) + '%',
        textposition='auto',
        marker_color='darkblue'
    )
)

fig.update_layout(
    title='Count of Bookings by Market Segment Type',
    xaxis_title='Market Segment Type',
    yaxis_title='Count of Bookings',
    font=dict(
        family='Arial, sans-serif',
        size=14,
        color='#000000'
    )
)

fig.show()
In [22]:
import plotly.express as px

# Group the data by market segment type and count the number of bookings for each booking status
market_segment_data = df_clean.groupby(['booking_status', 'market_segment'])['Booking_ID'].count().reset_index()

# Calculate the percentage of bookings for each market segment
market_segment_data['percentage'] = (market_segment_data['Booking_ID'] / market_segment_data.groupby(['booking_status'])['Booking_ID'].transform('sum')) * 100

# Create a bar chart of the count of bookings by market segment type and booking status
fig = px.bar(market_segment_data,
             x='market_segment',
             y='Booking_ID',
             color='booking_status',
             facet_col='booking_status',
             text=market_segment_data['percentage'].round(2).astype(str) + '%',
             labels={
                 'market_segment': 'Market Segment Type',
                 'Booking_ID': 'Count of Bookings'
             },
             title='Count of Bookings by Market Segment Type and Booking Status',
             color_discrete_sequence=['#f9bc86', '#a3acff']
)

fig.update_traces(textposition='auto')

fig.show()
In [23]:
fig = px.histogram(df_clean,
                   x="lead_time",
                   labels={
                       'lead_time': 'Lead Time (days)',
                       'count': 'Count'
                   },
                   title='Distribution of Bookings by Lead Time')

fig.update_traces(marker_color='#1f77b4')
fig.show()
In [24]:
fig = px.histogram(df_clean,
                   x="lead_time",
                   y="arrival_date_time",
                   labels={
                       'lead_time': 'Lead Time',
                       'arrival_date_time': 'Arrival Date Time'
                   },
                   title='Bookings by Lead Time and Arrival Date Time')
fig.show()
In [25]:
import pandas as pd
import plotly.express as px
import plotly.graph_objs as go

# Convert 'arrival_date_time' to datetime format
df_clean['arrival_date_time'] = pd.to_datetime(df_clean['arrival_date_time'], errors='coerce')

# Filter out rows with missing or invalid datetime values
df_clean = df_clean.dropna(subset=['arrival_date_time'])

# Extract month and year from 'arrival_date_time'
df_clean['month_year'] = df_clean['arrival_date_time'].dt.strftime('%b %Y')

# Group the DataFrame by 'month_year' and calculate the mean of 'lead_time'
mean_lead_time = df_clean.groupby('month_year')['lead_time'].mean().reset_index()

# Create bar chart
fig = px.bar(mean_lead_time, x='lead_time', y='month_year', orientation='h', 
             title='Average Lead Time by Arrival Month Year')

# Define the desired order of categories in the y-axis
desired_order = sorted(mean_lead_time['month_year'].unique(), key=lambda x: pd.to_datetime(x, format='%b %Y'))

# Update layout
fig.update_layout(
    font=dict(
        family='Arial, sans-serif',
        size=14,
        color='#000000'
    ),
    xaxis_title='Average Lead Time (days)',
    yaxis_title=None,
    yaxis=dict(categoryorder='array', categoryarray=desired_order)
)

# Add average lead time as text on the bars
fig.update_traces(text=mean_lead_time['lead_time'].round(2), textposition='inside')

fig.show()
In [26]:
# Create a box plot showing the lead time distribution for each market segment
fig = px.box(df_clean,
             x='market_segment',
             y='lead_time',
             title='Lead Time Distribution by Market Segment')

# Show the figure
fig.show()
In [ ]:
# Group the data by market segment and lead time
market_lead_data = df_clean.groupby(['market_segment', 'lead_time'
                                     ]).size().reset_index(name='count')

# Create the subplots
fig = make_subplots(rows=1,
                    cols=len(market_lead_data['market_segment'].unique()),
                    shared_yaxes=True)

# Add each subplot
for i, segment in enumerate(market_lead_data['market_segment'].unique()):
    data = market_lead_data[market_lead_data['market_segment'] == segment]
    fig.add_trace(go.Scatter(x=data['lead_time'],
                             y=data['count'],
                             mode='lines',
                             name=segment),
                  row=1,
                  col=i + 1)

# Update the layout and display the figure
fig.update_layout(title='Bookings by Market Segment and Lead Time',
                  xaxis_title='Lead Time',
                  yaxis_title='Count')
fig.show()
In [27]:
# Create two histograms showing the distribution of no_of_weekend_nights and no_of_week_nights
fig1 = px.histogram(df_clean,
                    x='no_of_weekend_nights_booked',
                    nbins=20,
                    title='Distribution of No. of Weekend Nights')
fig2 = px.histogram(df_clean,
                    x='no_of_week_nights_booked',
                    nbins=20,
                    title='Distribution of No. of Week Nights')

# Create a figure with two subplots
fig = make_subplots(rows=1,
                    cols=2,
                    subplot_titles=('Weekend Nights', 'Week Nights'))

# Add the histograms to the subplots
fig.add_trace(fig1['data'][0], row=1, col=1)
fig.add_trace(fig2['data'][0], row=1, col=2)

# Update the layout
fig.update_layout(showlegend=False)

# Show the figure
fig.show()
In [28]:
# Create two histograms showing the distribution of no_of_weekend_nights and no_of_week_nights
fig1 = px.histogram(df_clean,
                    x='no_of_weekend_nights_booked',
                    nbins=17,
                    title='Distribution of No. of Weekend Nights')
fig2 = px.histogram(df_clean,
                    x='no_of_week_nights_booked',
                    nbins=17,
                    title='Distribution of No. of Week Nights')

# Set the x-axis range for both subplots
x_axis_range = (0, 17)

# Create a figure with two subplots
fig = make_subplots(rows=1,
                    cols=2,
                    subplot_titles=('Weekend Nights', 'Week Nights'))

# Add the histograms to the subplots
fig.add_trace(fig1['data'][0], row=1, col=1)
fig.add_trace(fig2['data'][0], row=1, col=2)

# Update the layout of the subplots
fig.update_xaxes(range=x_axis_range, row=1, col=1)
fig.update_xaxes(range=x_axis_range, row=1, col=2)

# Update the layout of the figure
fig.update_layout(showlegend=False)

# Show the figure
fig.show()
In [29]:
# Create a scatter plot for no_of_week_nights
fig1 = px.scatter(
    df_clean,  
    x='average_room_price', 
    y='no_of_week_nights_booked',  
    color='meal_plan_type',  
    size='no_of_adults',  
    hover_data=['reserved_room_type']
)

# Set the title and axis labels
fig1.update_layout(
    title='Scatter Plot of Average Room Price and Number of Week Nights',
    xaxis_title='Average Room Price',
    yaxis_title='Number of Week Nights'
)

# Show the plot
fig1.show()

# Create a scatter plot for no_of_weekend_nights
fig2 = px.scatter(
    df_clean,  
    x='average_room_price', 
    y='no_of_weekend_nights_booked',  
    color='meal_plan_type',  
    size='no_of_adults',  
    hover_data=['reserved_room_type']
)

# Set the title and axis labels
fig2.update_layout(
    title='Scatter Plot of Average Room Price and Number of Weekend Nights',
    xaxis_title='Average Room Price',
    yaxis_title='Number of Weekend Nights'
)

# Show the plot
fig2.show()
In [30]:
# Group the data by no_of_weekend_nights and lead time
weekend_lead_data = df_clean.groupby(['no_of_weekend_nights_booked', 'lead_time'
                                      ]).size().reset_index(name='count')

# Group the data by no_of_week_nights and lead time
week_lead_data = df_clean.groupby(['no_of_week_nights_booked', 'lead_time'
                                   ]).size().reset_index(name='count')

# Create the subplots
fig = make_subplots(rows=1, cols=2, shared_yaxes=True)

# Add each subplot
fig.add_trace(go.Scatter(x=weekend_lead_data['lead_time'],
                         y=weekend_lead_data['count'],
                         mode='markers',
                         name='Weekend Nights'),
              row=1,
              col=1)
fig.add_trace(go.Scatter(x=week_lead_data['lead_time'],
                         y=week_lead_data['count'],
                         mode='markers',
                         name='Week Nights'),
              row=1,
              col=2)

# Update the layout and display the figure
fig.update_layout(title='Bookings by Lead Time and No. of Nights',
                  xaxis_title='Lead Time',
                  yaxis_title='Count')
fig.show()
In [31]:
# Calculate correlation matrix
corr_matrix = df_clean[num_cols].corr()

# Filter matrix to show only strong positive and negative correlations
strong_corr_matrix = np.where((corr_matrix >= 0.5) | (corr_matrix <= -0.5),
                              corr_matrix, np.nan)

# Plot heatmap
fig = px.imshow(strong_corr_matrix,
                labels=dict(x="Numeric Variables", y="Numeric Variables"),
                x=num_cols,
                y=num_cols,
                color_continuous_scale='RdBu',
                width=1000,
                height=700,
                zmin=-1,
                zmax=1,
                title='Correlation')

fig.update_layout(title='Correlation Between Numeric Variables', title_x=0.5)

# Add annotations to heatmap squares
annotations = []
for i in range(len(num_cols)):
    for j in range(len(num_cols)):
        if i != j:
            if np.isnan(strong_corr_matrix[i][j]):
                text = "-"
            else:
                text = f"{strong_corr_matrix[i][j]:.2f}"
            annotations.append(
                dict(x=num_cols[j], y=num_cols[i], text=text, showarrow=False))
fig.update_layout(annotations=annotations)

fig.show()
In [32]:
fig = px.scatter(
    df_clean,
    x='total_successful_bookings',
    y='repeated_guest',
    trendline='ols',
    trendline_color_override='red',
    title=
    'Relationship between No. of Previous Bookings Not Canceled and Repeated Guest'
)

fig.update_layout(xaxis_title='No. of Previous Bookings Not Canceled',
                  yaxis_title='Repeated Guest')

fig.show()
In [33]:
df_clean.columns
Out[33]:
Index(['Booking_ID', 'no_of_adults', 'no_of_children',
       'no_of_weekend_nights_booked', 'no_of_week_nights_booked',
       'meal_plan_type', 'has_parking_space', 'reserved_room_type',
       'lead_time', 'arrival_year', 'arrival_month', 'arrival_date',
       'market_segment', 'repeated_guest', 'total_cancellations',
       'total_successful_bookings', 'average_room_price',
       'total_special_requests', 'booking_status', 'arrival_date_time',
       'month_year'],
      dtype='object')
In [34]:
# Define the list of categorical columns to plot
cat_cols = ['meal_plan_type', 'reserved_room_type', 'market_segment']

# Create a subplot for each column
fig = make_subplots(rows=1, cols=len(cat_cols), subplot_titles=cat_cols)

# Add each categorical variable countplot to the subplot
for i, col in enumerate(cat_cols):
    # Compute the count for each category
    data = df_clean[col].value_counts().reset_index()
    data.columns = [col, 'count']
    # Add the countplot to the subplot
    fig.add_trace(px.bar(data, x=col, y='count').data[0], row=1, col=i + 1)

# Update the layout and display the figure
fig.update_layout(title='Distribution of Categorical Variables',
                  showlegend=False)
fig.show()

Data source:https://www.kaggle.com/datasets/ahsan81/hotel-reservations-classification-dataset